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Densely Connected Neural Networks for Nonlinear Regression
Densely connected convolutional networks (DenseNet) behave well in image processing. However, for regression tasks, convolutional DenseNet may lose essential information from independent input features. To tackle this issue, we propose a novel DenseNet regression model where convolution and pooling...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317522/ https://www.ncbi.nlm.nih.gov/pubmed/35885098 http://dx.doi.org/10.3390/e24070876 |
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author | Jiang, Chao Jiang, Canchen Chen, Dongwei Hu, Fei |
author_facet | Jiang, Chao Jiang, Canchen Chen, Dongwei Hu, Fei |
author_sort | Jiang, Chao |
collection | PubMed |
description | Densely connected convolutional networks (DenseNet) behave well in image processing. However, for regression tasks, convolutional DenseNet may lose essential information from independent input features. To tackle this issue, we propose a novel DenseNet regression model where convolution and pooling layers are replaced by fully connected layers and the original concatenation shortcuts are maintained to reuse the feature. To investigate the effects of depth and input dimensions of the proposed model, careful validations are performed by extensive numerical simulation. The results give an optimal depth (19) and recommend a limited input dimension (under 200). Furthermore, compared with the baseline models, including support vector regression, decision tree regression, and residual regression, our proposed model with the optimal depth performs best. Ultimately, DenseNet regression is applied to predict relative humidity, and the outcome shows a high correlation with observations, which indicates that our model could advance environmental data science. |
format | Online Article Text |
id | pubmed-9317522 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93175222022-07-27 Densely Connected Neural Networks for Nonlinear Regression Jiang, Chao Jiang, Canchen Chen, Dongwei Hu, Fei Entropy (Basel) Article Densely connected convolutional networks (DenseNet) behave well in image processing. However, for regression tasks, convolutional DenseNet may lose essential information from independent input features. To tackle this issue, we propose a novel DenseNet regression model where convolution and pooling layers are replaced by fully connected layers and the original concatenation shortcuts are maintained to reuse the feature. To investigate the effects of depth and input dimensions of the proposed model, careful validations are performed by extensive numerical simulation. The results give an optimal depth (19) and recommend a limited input dimension (under 200). Furthermore, compared with the baseline models, including support vector regression, decision tree regression, and residual regression, our proposed model with the optimal depth performs best. Ultimately, DenseNet regression is applied to predict relative humidity, and the outcome shows a high correlation with observations, which indicates that our model could advance environmental data science. MDPI 2022-06-25 /pmc/articles/PMC9317522/ /pubmed/35885098 http://dx.doi.org/10.3390/e24070876 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jiang, Chao Jiang, Canchen Chen, Dongwei Hu, Fei Densely Connected Neural Networks for Nonlinear Regression |
title | Densely Connected Neural Networks for Nonlinear Regression |
title_full | Densely Connected Neural Networks for Nonlinear Regression |
title_fullStr | Densely Connected Neural Networks for Nonlinear Regression |
title_full_unstemmed | Densely Connected Neural Networks for Nonlinear Regression |
title_short | Densely Connected Neural Networks for Nonlinear Regression |
title_sort | densely connected neural networks for nonlinear regression |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317522/ https://www.ncbi.nlm.nih.gov/pubmed/35885098 http://dx.doi.org/10.3390/e24070876 |
work_keys_str_mv | AT jiangchao denselyconnectedneuralnetworksfornonlinearregression AT jiangcanchen denselyconnectedneuralnetworksfornonlinearregression AT chendongwei denselyconnectedneuralnetworksfornonlinearregression AT hufei denselyconnectedneuralnetworksfornonlinearregression |